Hindawi Publishing CorporationAdvances in Civil EngineeringVolume 2012, Article ID 402179, 10 pagesdoi:10.1155/2012/402179

Research Article

Dual Mode Sensing with Low-Profile Piezoelectric Thin WaferSensors for Steel Bridge Crack Detection and Diagnosis

Lingyu Yu,1 Sepandarmaz Momeni,2 Valery Godinez,2

Victor Giurgiutiu,1 Paul Ziehl,3 and Jianguo Yu3

1 Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA2 Mistras Group Inc., 195 Clarksville Road, Princeton Junction, NJ 08550, USA3 Department of Civil Engineering, University of South Carolina, Columbia, SC 29208, USA

Correspondence should be addressed to Lingyu Yu, yu3@cec.sc.edu

Received 30 May 2011; Accepted 6 September 2011

Academic Editor: Piervincenzo Rizzo

Copyright © 2012 Lingyu Yu et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Monitoring of fatigue cracking in steel bridges is of high interest to many bridge owners and agencies. Due to the variety ofdeterioration sources and locations of bridge defects, there is currently no single method that can detect and address the potentialsources globally. In this paper, we presented a dual mode sensing methodology integrating acoustic emission and ultrasonic waveinspection based on the use of low-profile piezoelectric wafer active sensors (PWAS). After introducing the research backgroundand piezoelectric sensing principles, PWAS crack detection in passive acoustic emission mode is first presented. Their acousticemission detection capability has been validated through both static and compact tension fatigue tests. With the use of coaxialcable wiring, PWAS AE signal quality has been improved. The active ultrasonic inspection is conducted by the damage index andwave imaging approach. The results in the paper show that such an integration of passive acoustic emission detection with activeultrasonic sensing is a technological leap forward from the current practice of periodic and subjective visual inspection and bridgemanagement based primarily on history of past performance.

1. Introduction

According to the Federal Highway Administration (FHWA)National Bridge Inventory (NBI) of 2007, the number ofstructurally deficient and functionally obsolete bridges is72,524 and 79,792, respectively [1]. While there are about10,000 bridges being constructed, replaced, or rehabilitatedannually in the United States at a cost of over $5 billion, thetotal annual costs including maintenance and routine oper-ation are significantly higher [1]. As the inventory continuesto age, routine inspection practices will not be sufficient forthe timely identification of areas of concern and to provideenough information to bridge owners to make informeddecisions for safety and maintenance prioritization. Contin-uous monitoring is therefore desirable for long-term evalua-tion; monitoring areas of concern, such as retrofits or previ-ous repairs or monitoring an area with known flaws, beforea scheduled inspection. Continuous monitoring can alsobe used in cases where there is a concern about vandalism,

terrorism, and/or bridge element integrity. Therefore, mon-itoring of fatigue cracking in steel bridges is of interest tomany bridge owners and agencies.

To address this urgent need, the authors are conduct-ing research on novel and promising sensing approachestogether with energy harvesting devices to reduce the dra-matic uncertainty inherent into steel bridge inspection andmaintenance plan [2]. One of the challenges in this researchis focused on the development of dual use piezoelectric waferactive sensors for fatigue crack detection. The combinedschematic uses acoustic emission to detect the presence offatigue cracks in their early stage while active sensing allowsfor the imaging and quantification of cracks using minimumnumber of sensors.

1.1. Crack Detection on Steel Bridge Structures. The moni-toring of fatigue cracking in bridges has been approachedwith acoustic emission using either resonant or broadband

2 Advances in Civil Engineering

PWASta

tby = +d

t = 2d

y = −d −a +a

τ(x)eiωt

x

Figure 1: PWAS and structure interaction through the interfacelayer.

sensors. Acoustic emission monitoring is capable of detectingcrack growth behavior [3–7] and assessing the integrity ofstructures such as bridges and aircraft [8, 9]. Acoustic emis-sion data has recently been directly related to crack growthrate in representative steel compact tension (CT) specimens.This development holds promise for the prognosis of in-service bridges [10]. The method has the notable advantagethat the precise location of cracking does not need to beknown for evaluation purposes. Rather, the sensors togetherwith appropriate algorithms are capable of locating andquantifying active crack activity. It has been reported thatacoustic emission techniques are so sensitive that fatiguecracks can be detected successfully even though the cracklength may be less than 10 μm [5, 7]. However, one of thechallenges in passive monitoring is that the acoustic emissionrelies on an active crack growing process. Also, thoughacoustic emission sensing can detect a crack at its very earlystage, it generally cannot provide information about thecrack size or crack growth rate unless an initial size isprovided.

Historically, AE signals have been captured with speciallydesigned and fabricated AE sensors. Conventional AE sen-sors are made of piezoelectric crystals as the sensing elementswhich are encapsulated for protection and coupled togetherwith a wear plate for good acoustic coupling. The frequencycontent and sensitivity of the sensor are controlled by thegeometry and properties of the piezoelectric crystal as wellas the housing for the crystal.

1.2. Piezoelectric Wafer Active Sensors. Piezoelectric waferactive sensors (PWAS) can function as an active sensingor passive device or network using piezoelectric principlesand provide a correlation between mechanical and electricalvariables. They can be permanently attached to the structureto monitor condition at will and can operate in active guidedwave interrogation or passive AE sensing modes. The trans-mission of actuation and sensing between the PWAS andthe host structure is achieved through the bonding adhesivelayer. The adhesive layer (Figure 1) acts as a shear layer, inwhich the mechanical effects are transmitted through sheareffects [11].

An important characteristic of PWAS, which distin-guishes them from conventional ultrasonic transducers, istheir capability of exciting multiple guided Lamb wavemodes at a single frequency. There are at least two Lambmodes, A0 and S0, existing simultaneously when the product

of the wave frequency and structure thickness ( f · dproduct) falls in the range of 0∼1 MHz-mm. At larger f · dproduct values, more modes are present. In addition, dueto the intrinsic dispersion property, the Lamb wave modespropagate at different speeds and the speeds change withfrequency, which complicates the signal interpretation fordamage detection. A single mode that is sensitive to thedamage is desired for most of the SHM applications. Thismay be attained through wave tuning [12]. The process ofwave tuning attempts to modify the excitation parametersto excite a certain mode for detection of a specified type orinstance of damage [12]. By carefully selecting PWAS lengthat either an odd or even multiple of the half wavelength,a complex pattern of strain maxima and minima emerges(Figure 2). Since several Lamb modes, each with its owndifferent wavelength, coexist at the same time, a selectedLamb mode can be tuned by choosing the appropriatefrequency and PWAS dimensions.

An example of PWAS tuning is presented in Figure 2for a 7-mm square PWAS installed on a 1-mm aluminumalloy 2024-T3 plate. The experimental amplitude plot inFigure 2(a) shows that for the plate being studied, a S0 tuningfrequency around 200 kHz can be identified, where theamplitude of the A0 mode is minimized while that of the S0mode is still strong. Therefore, by choosing the excitation fre-quency, a single mode can be obtained for damage detection[13]. Theoretical prediction given in Figure 2(b) is consistentwith the experimental results.

1.3. PWAS Dual Mode Sensing toward Field Application.Dupont et al. [14] have demonstrated the possibility ofusing embedded piezoelectric thin wafers to detect AEsignals in composite materials. In the subject project, we aredeveloping a dual mode sensing approach using the low-profile wireless PWAS network with energy harvested fromwind and/or ambient vibration energy [15]. To minimize theenergy consumption, on one hand, it is envisioned that asfew as four PWAS will be employed to monitor the crackgrowth. On the other hand, the dual mode sensing allowsPWAS to operate in passive AE mode throughout the entiremonitoring process unless significant AE events are detected,indicating major crack presence in the structure. When anAE event is identified, the PWAS can be switched to activemode to interrogate the bridge structure with propagatingguided waves to assess the crack size and location. Eventually,an entire steel bridge may be mapped with the guided waveinterrogation with visualized crack damage.

2. PWAS Passive Mode AcousticEmission Detection

In the subject project, we adapted PWAS as AE sensors todetect stress waves with frequency components concentratedat 150 kHz where the acoustic signals propagating with min-imal attenuation and background noise due to the rubbingof structural components.

2.1. PWAS as AE Sensor. Laboratory tests have been con-ducted to investigate the PWAS application as an AE sensor.

Advances in Civil Engineering 3

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700

Frequency (kHz)

Vol

ts(m

V)

A0

S0

A0S0

(a)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 100 200 300 400 500 600 700

f (kHz)

A0

S0

Stra

in

(b)

Figure 2: Lamb wave mode tuning on a 1-mm thick aluminum alloy 2024-T3 using 7-mm PWAS. (a) Experimental wave amplitude within0∼700 kHz; (b) predicted strain curves [13].

A typical commercial R15I (http://www.pacndt.com/down-loads/Sensors/Integral%20Preamp/R15I-AST.pdf) AE sensorwas used to calibrate the measurements. Two specimens wereused in the tests. One was a 1.6 mm thick 2024 aluminumplate and the other was a 19 mm thick A572 grade 50structural steel panel. Both specimens were installed with7-mm diameter 0.2 mm thick round PWAS Material APC850 by Americanpiezo (http://www.americanpiezo.com/apc-materials/choosing.html) using M-200 bonding adhesivefollowing the standard installation used by strain gauge. TheR15I sensor was mounted using hot glue.

The test setup is illustrated in Figure 3. PAC DiSP (http://www.pacndt.com/index.aspx?go=products&focus=/multi-channel//disp.htm) system was used to perform data acqui-sition. PWAS was connected to a preamp then to channel 1.The preamp had a 100–1200 kHz built-in filter and couldaccommodate a signal amplification of 40 dB. The R15Isensor had a built-in internal preamp with a gain also at40 dB and was connected to channel 2. AE events wereintroduced by pencil lead break (PLB). Rubber gloves wereused to avoid causing electrical disturbance by touching theplate.

2.1.1. Aluminum Plate Tests. In the first part of the work, a1.6-mm thick aluminum plate, approximately 300-mm by300-mm, was used for testing PWAS AE detection. PWASand R15I were placed adjacently on the plate, about 165 mmaway from the plate edge where the PLB was applied. Intotal, five PLB of various lead sizes were applied. The PWAStransducer detected all of them with comparable amplitudesto those captured by R15I, as summarized in Table 1.

Figure 4 shows the waveforms and the frequency spectrafor the test described in the third row in Table 1. In this test,0.5 mm HB pencil lead was broken at the edge of the platein the out-of-plane direction (pressing down and springingup). The detected AE signal amplitudes of the two sensorswere about the same (78 dB). The PWAS gave a peak signal of

Table 1: AE detection on the 1.6-mm aluminum plate.

PLB size PWAS AE amplitude (dB) R15I AE amplitude (dB)

0.7 mm 83 82

0.5 mm 89 89

0.5 mm 78 78

0.3 mm 88 86

0.3 mm 84 78

2200 mV against a noise of 200 mV, resulting in a signal-to-noise ratio (SNR) of approximately 11 or 21 in dB. Comparedto R15I waveform, the response of PWAS was conspicuouslycrisper in the earlier part corresponding to the S0 modearrival which was followed by the arrival of slower A0 mode.

Looking at the AE waveforms by PWAS and R15I presentin Figure 4, it is noticed though the two waveforms havecomparable signal peak amplitudes, PWAS shows higherfloor noise (circled part). For AE detection, it is needed thatthe sensor shows good signal-to-noise ratio. In this case, floornoise for PWAS is too high and needs to be decreased.

An expanded view of the early PWAS response is shownin Figure 5(a). The fast S0 mode and slow but highlydispersive A0 mode are clearly present. The out-of-planedisplacement waveform generated by PlotRLQ (PlotRLQ is acomputer program that computes theoretical waveforms bysumming the contributions of Lamb wave modes excited byspecified moment tensor sources at specified depths withinthe plate based on classic Lamb wave theory) calculatedfor this situation is shown in Figure 5(b). The agreementbetween PWAS waveform and theoretical prediction isdiscernable, especially for the S0 mode, the earliest part ofthe response. The small discrepancy comes from the raggedappearance at the beginning of the A0 mode and fromthe relatively abrupt falloff after the A0 reaches its peakamplitude. This may be credited to the PWAS sensor apertureeffect as described in [12].

4 Advances in Civil Engineering

PWAS

R15I

Preamp CH1

CH2

Testing specimen

DiSP

(a)

PWASSpecimen

R15IPreamp

DiSP

(b)

Figure 3: AE testing setup. (a) Test setup schematic; (b) laboratory test setup.

f (kHz)

PWAS

t (μs)

0 100 200 300 400 500 600 700 800 900 1000

−2

−1

0

1

2

Vol

t

100 200 300 400 500

0.02

0.04

0.06

|A|

(a)

f (kHz)

R15I

t (μs)

0 100 200 300 400 500 600 700 800 900 1000

−1

0

1

Vol

t

100 200 300 400 500

0.02

0.04

0.06

0.08

0.1

|A|

(b)

Figure 4: 0.5 mm PLB detection on 1.6-mm aluminum plate. (a) PWAS AE waveform and its frequency spectrum; (a) R15I AE waveformand its frequency spectrum.

−0.004

−0.003

−0.002

−0.001

0

0.001

0.002

0.003

0.004

20 40 60 80 100 120 140

S0

A0

(a)

−0.004

−0.003

−0.002

−0.001

0

0.001

0.002

0.003

0.004

20 40 60 80 100 120 140

S0

A0

(b)

Figure 5: PWAS response compared to theoretical prediction. (a) PWAS waveform; (b) theoretical out-of-plane displacement by PlotRLQ.

Advances in Civil Engineering 5

0 100 200 300 400 500 600 700 800 900 1000−0.6

−0.4

−0.2

0

0.2

0.4

Vol

t

100 200 300 400 500

1

2

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×10−3

PWAS

f (kHz)

t (μs)

|A|

(a)

Vol

t

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0 100 200 300 400 500 600 700 800 900 1000

t (μs)

R15I

−2

−1

0

1

2

0.02

0.04

0.06

|A|

(b)

Figure 6: 0.5 mm PLB detection on 19-mm steel plate. (a) PWAS AE waveform and its frequency spectrum; (a) R15I AE waveform and itsfrequency spectrum.

R15I

12

9.512

3.25

0.25

PWAS AE sensor

(a) (b)

Figure 7: AE detection on a 1/2′′ CT specimen. (a) Geometry of the specimen and arrangement of transducers; (b) a snapshot of the actualspecimen. AE PWAS are circled (rest are for active sensing). The R15I were installed on the other side of the specimen.

2.1.2. Steel Plate Tests. The second part of the work wasconducted on a 19-mm thick steel plate. 0.5 mm HB PLB wasapplied on the surface of the plate about 72 mm away fromPWAS. The R15I transducer was glued on the plate with adistance of 98 mm from the PLB.

The PLB was detected by PWAS with an AE amplitudeof 73 dB in contrast to the 87 dB detected by R15I. Thewaveforms and their frequency spectra are provided inFigure 6. The PWAS signal showed a strong negative-goingspike accounting for the 73 dB amplitude, fitting in a 600 mVpeak. The background noise was about 150 mV, resulting in a

SNR of approximately 4 or about 12 in dB. The SNR of PWASin steel plate was much lower than that in aluminum plate.

By examining the frequency spectra, it can be noted thatPWAS has major frequency components beyond 200 kHz,showing a wider frequency response compared to resonanttype R15I AE sensor.

2.2. PWAS AE Detection in Compact Tension Testing. Com-pact tension (CT) specimens made of the same material asthe steel plate used in Section 2.1 were used. The geometryof the specimens is displayed in Figure 7. Custom fixtures

6 Advances in Civil Engineering

R15 group

PWAS group

Crack opening

0.8 mm

1.63 mm

1

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1E+002

1E+004

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1E+008

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

0 5000 10000 15000 20000

−2

0

2

Absolute energy (aJ) versus time (s) ⟨all channels⟩ loc[1]

Absolute energy (aJ) versus time (s) ⟨all channels⟩ loc[2]

Parametric 3(mm) versus time (s) ⟨all channels⟩

1E+000

(a)

0 5 10

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Y position versus X position versus energy ⟨all channels⟩ loc[1]

(b)

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9 10

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7 6

Y position versus X position versus energy ⟨all channels⟩ loc[2]

(c)

Figure 8: Comparison of crack localization in CT test on 1/2′′ steel specimen. (a) Cumulative acoustic energy by PWAS and R15I; (b)cracking detection and localization by R15I; (c) cracking detection and localization by PWAS.

were designed and fabricated to mount the CT specimens.The cyclic tension loads of minimum 1 KN and maximum50 KN were applied to the specimen using an MTS810servohydraulic mechanical testing machine. Fatigue testswere conducted under load-controlled mode with frequencyof 1 HZ. A clip gage was employed to measure the crackmouth opening displacement (CMOD) to clarify crackopening and closure and to determine the magnitude of theCMOD. The surface cracks were also monitored opticallywith a high-resolution recording microscope. Two separatesets of AE sensors, namely, R15I and PWAS monitored theprocess, as well. They connected to the Sensor Highway II

(http://www.pacndt.com/products/Remote%20Monitoring/AE Sensor Highway.pdf) data acquisition system throughpreamps. The data from these two sets of sensors were ana-lyzed separately with AEwin (http://www.pacndt.com/index.aspx?go=products&focus=/software/aewin.htm) software.

The results of crack localization from PWAS sensorsand R15I during CT testing are shown in Figure 8. R15Isensors detected 1,171 AE events prior to failure while PWASdetected only 54 events. Figure 8(a) gives the cumulativeacoustic energy of R15I and PWAS together with the crackopening displacement. While PWAS detected a fewer numberof acoustic activities, they detected the crack growth when

Advances in Civil Engineering 7

Figure 9: PWAS installation using a coaxial cable.

f (kHz)

t (μs)

0 200 400 600 800 1000

−0.2

0

0.2

Vol

t

100 200 300 400 500

5

10

15

×10−4

|A|

PWAS AE waveform

Figure 10: PLB detection on steel plate using coaxial cable wiredPWAS.

the crack size reaches 0.83 mm. As can be seen in Figure 8(b),PWAS localization was closer and concentrated aroundthe crack tip compared with the R15I detection inFigure 8(c). This was thought to be mainly due to theenhanced sensitivity of the R15I sensors, which can compli-cate source location in small-scale laboratory specimens dueto reflections.

2.3. PWAS Adaptation toward Field Application. In the vali-dation tests, it has been noticed that PWAS on steel speci-mens exhibited higher floor noise compared to the standardR15I AE sensors, therefore, providing a poor SNR ratio.To enhance the signal quality toward field application anddecrease the background floor noise, we improved PWASinstallation by using a coaxial cable similar to that used forR15I sensors. The shield of the coax was connected to thesteel plate very close to the PWAS while the center conductorwas connected to the positive electrode of the PWAS, asshown in Figure 9.

Detection of a 0.3 mm PLB about 20 mm away from a co-axial cable wired PWAS was evaluated (Figure 10). The rest

of the setup remained the same as for the tests presentedin Section 2.1.2. The resulting waveform had an amplitudeof 71 dB, approximately 400 mV. The background floornoise was discernibly decreased and approximated at 10 mV.Therefore, SNR was measured at 40 or 32 dB, significantlyimproved compared to the 12 dB presented in Section 2.1.2.

3. PWAS Active Mode Crack Sensing

In the dual mode sensing schematic, after significant crackinghas been identified by passive mode AE detection, activemode sensing using pitch-catch interrogation is evoked toquantify crack growth through damage index and arrayimaging. A PWAS network consisting of several sensorsspatially distributed on the plate can be used to interrogatethe plate with one sensor generating the guided wave andthe others receiving the structural response. When an elasticLamb wave is transmitted and travels through the structure,wave scattering occurs in all directions where there is achange in the material properties due to damage. The scattersignal is defined as the difference between the measurementduring the development of damage and the baseline signalat the initial stage. One advantage of using scatter signalsis to minimize the influence caused by boundaries or otherstructural features which would otherwise complicate theLamb wave analysis.

3.1. Damage Index Evaluation. We assume that cracking isthe sole source of changes in the detected Lamb waves. Alsobeing assumed is that the waves travel in straight paths inthe plate structures. Hence, the objective of our Lamb wavesignal analysis is to extract damage-related characteristicsfrom the measured sensory data. In this research damageindex (DI) is defined as [16]

DI = 1− CXY

σXσY. (1)

CXY is the covariance of X and Y given by

CXY =N∑

j=1

(X j − μX

)(Y j − μY

). (2)

where μ is the mean value and N is the length of thedataset. σX and σY are the standard deviations of X and Y ,respectively, with their product given as

σXσY =√√√√√

N∑

j=1

(X j − μX

)2(Y j − μY

)2. (3)

For the active sensing implemented during the CT testpresented in Section 2.2, four PWAS were used to performpitch-catch wave propagation interrogation, as marked andnumbered in Figure 11(a). Using the definition of damageindex defined above, the DI curves were plotted at differentcrack length as shown in Figure 11(b) for all the pitch-catchpaths. The DI increases when the crack grows. The detectionalong sensors P0 to P1 is most sensitive, followed by the one

8 Advances in Civil Engineering

P1 P2

P0 P3

(a)

P0P1P0P2P0P3

P1P2P1P3P2P3

DI

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

Crack length (mm)

(b)

Figure 11: AE detection on a 1/2′′ CT specimen. (a) Geometry of the specimen and arrangement of transducers; (b) a snapshot of the actualspecimen. AE PWAS are circled (rest are for active sensing). The R15I was installed on the other side of the specimen.

(a)

0 80 160 240 320 400 4800

60

120

180

240

300

360

(b)

Figure 12: Array imaging for crack growth detection. (a) Test specimen and sensor network; (b) array imaging of a hairline crack of 18 mmlength. Sensor locations are marked as green face circles.

along sensors P1 to P3 since the paths are perpendicular tothe crack development path. The increment of the DI curvesis also well correlated to the crack growth under fatigueloading.

3.2. Array Imaging. The array imaging methods have theincredible capability to map the structure and existingdamage in it, providing a means to qualitatively assess thestructural integrity. The sparse array uses scatter signalsfrom a network of sensors to construct a diagnosis image.The image construction is based on triangulation principleand conducted by shifting back the scatter signals at timequantities defined by the transmitter-receiver locations usedin the pitch-catch mode. Assuming a single damage scatteris located at point Z(x, y) in the structure, the scatter signal

from transmitter Ti to receiver Rj contains a single wavepacket caused by the damage. The total time of traveling τZ isdetermined by the locations of the transmitter Ti at (xi, yi),the receiver Rj at (x j , y j), and Z(x, y), as

τZ =√

(xi − x)2 +(yi − y

)2 +

√(x − x j

)2+(y − y j

)2

cg,

(4)

where cg is the group velocity of the traveling Lamb wave,assuming constant. When a wave packet is shifted back by thequantity defined by the transducers and the exact positionof the damage, that is, τZ , ideally the peak will be shiftedright back to the time origin. If the wave packet is shiftedby a quantity defined with otherwise cases (such as τi and

Advances in Civil Engineering 9

τO), the peak will not be shifted right at the time origin. Foran unknown damage, for a certain scatter signal with τZ , thepossible locations of the damage constitute an orbit of ellipsewith the transmitter and receiver as the foci. To locate thedamage, ellipses from other scatters (or transmitter-receiverpairs in the network) are needed (triangulation). For a givennetwork of M transducers, a total of M2 scatter signals will beused without considering reciprocity. The pixel value at thelocation Z(x, y) is defined as

PZ(t0) =M∏

i=1

M∏

j=1

si j(τZ), i /= j, (5)

where si j is the scatter signal obtained from ith transmitterand jth receiver. Details of principles and applications ofultrasonic array imaging can be found in [17].

The crack detection demonstration was conducted on a1-mm thick aluminum plate. The imaging was performed bya four-PWAS network to assess a simulated hairline crackcentered inside, shown in Figure 12(a). A hairline crack of18 mm was made on the plate, and then crack length wasincreased to 23 mm, resulting in a growth of 5 mm. TheLamb wave used to conduct the imaging was the S0 modeat 310 kHz with a wavelength about 17 mm.

The image result of the crack at 18 mm is shown inFigure 12(b), giving a clear detection with two highlightedspots representing the two crack tips; therefore, they couldbe used to estimate the crack length and monitor the crackgrowth. The first crack of 18 mm was estimated at 17.09 mmand the second one of 23 mm was estimated at 22.47 mm. Acrack growth of 5.38 mm was hence measured with the arrayimaging method with an error of about 7.6% compared tothe actual growth of 5 mm (from 18 mm to 23 mm).

4. Conclusions

Piezoelectric wafer active sensors (PWAS) have madetremendous progress in structural health monitoring duringthe past decade, but their exposure to civil infrastructurehas been little discussed so far. The work presented hereintends to explore PWAS applications for in-field monitoringof infrastructure (e.g., civil steel bridges) using both acousticemission and active wave propagation sensing. Laboratorydemonstration on both thin aluminum and thick steel plates,the PWAS have been proved as AE sensors. The use of coaxialwiring cables has greatly improved the PWAS waveformsignal-to-noise ratio, making it more suitable for field appli-cation. The PWAS AE sensing of fatigue cracking on a CTspecimen showed that it can provide concentrated detectionaround the crack tip with a relatively fewer numbers ofacoustic emission events than the R15I AE transducers. ThePWAS active mode sensing using propagating guided wavescan interrogate the structures at will and provide a clearindication and quantitative estimation of the crack growththrough damage index or array imaging. The dual modesensing of the presented PWAS methodology has shownits promising application to insitu health monitoring anddiagnosis of steel bridges.

Acknowledgments

This paper was performed under the support of the USDepartment of Commerce, National Institute of Standardsand Technology, Technology Innovation Program, Coop-erative Agreement number 70NANB9H9007. The authorswould also like to thank Dr. Adrian Pollock from MistrasGroup for his insightful comments on the presented passivesensing.

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